Research On Quality Of Experience For Streaming Media Service System

Along with deeply development of computer technology, compression technology and communications technology, the Streaming Media Business became fast developed and widespread used。But currently there are a lot of difficulties to transfer Streaming Media by Internet; the main reason is that the Internet was not designed for continuously connectionism Streaming Media transfer, but only for paroxysmal data transfer. For the purpose to efficiently transfer Streaming Media with high quality, there are many kinds of technology supports are needed, such as Streaming Media coding technique, QOS technique, fault-tolerant technique, simultaneous techniques and relevant agreements etc.Raditional network applications usually use net QOS parameter to describe the corresponding service quality, but cannot absolutely apply to Streaming Media Application System. In terms of Streaming Media service system, the final purpose of it is to provide sensible quality QOE for customers' satisfaction, sensible quality QOE can be much more precisely mirror the end-user satisfaction about Streaming Media service, such as if the image clear and fluently transfer, or the voice continuously transfer etc. Therefore direction of research is to achieve the Streaming Media service system control with end-user sensible quality QOE.The contents of research as follows:This article particularly presents the theory and technology of Streaming Media service quality control system, advances a theory of Streaming Media service system frame based on QOE sensible quality optimization. Which is the most important improvement from that frame is to join QOS network service parameter into QOE sensible service quality mapping module and Streaming Media distinguish module, service quality mapping module will make the user's QOE needs into the service needs of network's QOS, provide assistance to system on the quantitative analysis of QOE. Differentiated Services Module can offer the differentiated service on Media internal data according to the importance of Streaming Media contents, the differentiated service can also assist system to have a pointed revision on service part by cache memory scheduling, adaptive transmission, error control those streaming media control mechanism. As a primary objective the frame improves the quality of user's QOE perceived, and a wide range of practical value.After research, found the realization of a model which points against of QOS parameter to QOE sensible quality mapping model by the video Streaming Media transmission, also known as the transmission distortion model. Studied the common technology based on macro video streaming media codec technology, analysis of the video frame codec between the dependence caused by the proliferation of video streaming error distortion, the final combination of video streaming technology for a unified error analysis of hiding, to achieve the predictive coding based on video streaming media transmission distortion model. The model is able to codec algorithms and error concealment algorithm to determine the circumstances in the sending end through the network QOS parameters and parameter estimation video coding video streaming media QOE mass loss, can simultaneously control and transmission control algorithms and other quantitative indicators. QOS parameters of network services to QOE mapping perceived quality streaming media service module is the most important service module, as well as the video streaming services is the most complex and commonly used services. This research based on predictive coding of video streaming media transmission distortion model parameters to achieve the QOS service to the QOE perceived quality of the fuzzy mapping, to provide similar quantitative analysis for the current vast majority network of video streaming media applications.Research on the video streaming media congestion control mechanism, designed and implemented to optimize the QOE-based adaptive congestion control algorithm and improved algorithm. Algorithm based on quantitative analysis of video streaming media transmission from the QOS parameters to QOE perceived quality mapping , differentiated services based on macro block coding QOE perceived quality of the contribution. Algorithm based on the importance of hierarchical data processing parameters, in a time when deal with congestion control, as far as possible to ensure the best quality of QOE end. Experiments show that the control algorithm can effectively improve the performance of streaming media transmission and the image quality of the client, and it can also be effectively enhanced the QOE quality of video streaming.Research on the video streaming media transmission error control mechanism, a new algorithm was designed and implemented to optimize the QOE-based adaptive FEC algorithm. Algorithm based on quantitative analysis of video streaming media transmission from the QOS parameters to QOE perceived quality mapping , differentiated services based on macro block coding QOE perceived quality of the contribution. Mainly including the corresponding proportion of redundant data by the importance of coding for different macro block allocation, in accordance with the importance of encoding macro blocks to pack up the optimization strategy. Experiments show that the control algorithm can effectively improve the performance of streaming media transmission and the image quality of the client, and it can also be effectively enhanced the QOE quality of video streaming.